MULTISENSOR: Mining and Understanding of multilinguaL contenT for Intelligent Sentiment Enriched coNtext and Social Oriented inteRpretation. The consumption of large amounts of multilingual and multimedia content regardless of its reliability and cross-validation can have important consequences on the society. An indicative example is the current crisis of the financial markets in Europe, which has created an extremely unstable ground for economic transactions and caused insecurity in the population. The fact that the national mass media provide exaggerated and contradictory information and the inaptness to understand local contexts from different countries has a considerable share in the aggravation of the crisis.
To break this spiral, we need multilingual technologies with sentiment, social and spatiotemporal competence that are able to interpret, relate and summarise economic information and news created from various local subjective and biased views and disseminated via TV, radio, mass media websites and social media. To address these challenges, MULTISENSOR will mine heterogeneous data from the aforementioned resources and apply multidimensional content integration. MULTISENSOR will go beyond the state of the art by proposing the following scientific objectives:
a) content distillation of heterogeneous multimedia and multilingual data;
b) sentiment and context analysis of content and social interactions;
c) semantic integration of heterogeneous multimedia and multilingual data;
d) semantic reasoning and intelligent decision support;
e) multilingual and multimodal summarisation and presentation of the information to the user.
To achieve multidimensional integration of heterogeneous resources, MULTISENSOR proposes a content integration framework that builds upon multimedia mining, knowledge extraction, analysis of computer-mediated interaction, topic detection, semantic and multimodal representation as well as hybrid reasoning.
The developed technologies will be validated with the aid of 2 main use cases: a) International mass media news monitoring and b) SME internationalisation.
FP7 ICT STREP, 2013-2016